Academic Articles & Research Papers

An urban patrol case urgency classification method based on self-supervised clustering of a graph neural network.

Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for ...

Towards automated metaphase cell detection using foundation models: A SAM and DINO-based approach

Karyotyping is a widely used laboratory technique to detect abnormalities in chromosomes. Karyotyping consists of many steps, including collecting samples, cell culturing and metaphase cell detection. Cytogeneticists examine all the images ...

A comprehensive analytical assessment of dino-based architectures for sustainable automated potato inspection in the Al Kharj Region

Abstract Self-supervised learning (SSL) has emerged as a pivotal paradigm in computer vision, particularly for applications where labeled data are limited, costly, or labor-intensive to obtain. This paradigm is especially relevant to sustai...

Learning by Watching: A Review of Video-Based Learning Approaches for Robot Manipulation

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain regarding generalizability and real-world transfer. Meanwhile, large-scale ‘in-the-wi...

Approaches to Improve the Quality of Person Re-Identification for Practical Use

The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal ...

A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction

Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, ...

Vehicle Ego-Trajectory Segmentation Using Guidance Cues

Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One...

Attention mechanisms in computer vision: A survey

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an ...

High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution

Abstract Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and...

Explainable and reliable kidney CT image classification using self-supervised DeiT-Tiny transformer

IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...

Text Data Augmentation for Deep Learning

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augme...

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...

SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models.

A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tail...

Advancing morphological profiling with self-supervised learning through comparative insights into representation learning of cellular perturbations.

Raman Spectroscopy Pre-Trained Encoder: A Self-Supervised Learning Approach for Data-Efficient Domain-Independent Spectroscopy Analysis

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a...